This video introduces the notions of C-space (configuration space) obstacles, connected components of the free configuration space, and collision detection.

This video introduces graph representations of free C-space, including undirected and directed graphs, weighted and unweighted graphs, and trees.

This video describes A* graph search, one of the most popular and efficient methods for finding optimal paths in a graph.

This video introduces roadmap methods for complete path planning: if a path exists, then a roadmap method is guaranteed to find one. Such methods tend to be applied to only simple, low-dimensional problems, however. One example, given in the video, is path planning for a planar polygon translating among polygonal obstacles.

This video introduces grid methods for path planning, where the free C-space is represented by a regular grid that can be searched using standard graph search methods (e.g., A*). To increase efficiency, multi-resolution grids can also be employed.

This video introduces the popular sampling-based probabilistic roadmap (PRM) approach to motion planning.

This video introduces the popular sampling-based rapidly-exploring random trees (RRT) approach to motion planning.

This video introduces the virtual potential field method for reactive motion planning, where obstacles are at a high potential and the goal is at the minimum potential. The negative of the gradient of the potential is a force that pushes the robot away from obstacles and toward the goal.

This video is a brief introduction to the broad field of optimization-based approaches for robot motion planning.

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This video introduces different robot control objectives (motion control, force control, hybrid motion-force control, and impedance control) and typical block diagram models of controlled robots.

This video introduces the error response for a controlled system and characterizes the error response in terms of its steady-state error and its transient response (overshoot and settling time).

This video introduces linear error response, where the error dynamics are represented by a linear ordinary differential equation, which can also be represented as a set of coupled first-order differential equations, xdot = Ax. Stability of the error dynamics is achieved if the real components of the eigenvalues of A are all negative, or, equivalently,

This video studies error dynamics modeled as a first-order linear ordinary differential equation.

This video studies error dynamics modeled as a second-order linear ordinary differential equation. Stable error dynamics are characterized as overdamped, critically damped, or underdamped.

This video introduces proportional (P) control of the position of a single-degree-of-freedom system where the control input is a velocity.

This video introduces proportional-integral (PI) control of the position of a single-degree-of-freedom system, and feedforward plus PI feedback control, for the case where the desired position is a ramp as a function of time (constant velocity) and the control input is the velocity. The approach generalizes easily to the control of a multi-degree-of-freedom robot.

This video addresses task-space motion control of a robot, where the control inputs are the joint velocities and the desired motion of the end-effector is expressed as its configuration X in SE(3) and the end-effector velocity is expressed as a twist. The proposed control method is a feedforward plus PI feedback controller.

This video introduces proportional-integral-derivative (PID) control for a single robot joint, as well as PD control to a desired constant position, for the case where the control input is a joint torque or force.

This video compares PD vs. PID control for setpoint control of a single robot joint moving in gravity, where the control input is a torque.